Overview

Dataset statistics

Number of variables22
Number of observations18
Missing cells133
Missing cells (%)33.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory184.0 B

Variable types

Text2
Unsupported3
Categorical5
Numeric12

Alerts

Region has constant value ""Constant
Division has constant value ""Constant
Games_Level has constant value ""Constant
Qualifier has constant value ""Constant
Back Squat (lbs) is highly overall correlated with Deadlift (lbs) and 3 other fieldsHigh correlation
Clean and Jerk (lbs) is highly overall correlated with Fight Gone Bad and 2 other fieldsHigh correlation
Deadlift (lbs) is highly overall correlated with Back Squat (lbs) and 2 other fieldsHigh correlation
Fight Gone Bad is highly overall correlated with Clean and Jerk (lbs) and 4 other fieldsHigh correlation
Filthy 50 (s) is highly overall correlated with Back Squat (lbs) and 4 other fieldsHigh correlation
Fran (s) is highly overall correlated with Back Squat (lbs) and 2 other fieldsHigh correlation
Grace (s) is highly overall correlated with Back Squat (lbs) and 2 other fieldsHigh correlation
Helen (s) is highly overall correlated with Fran (s) and 3 other fieldsHigh correlation
Max Pull-ups is highly overall correlated with Helen (s) and 1 other fieldsHigh correlation
Rank is highly overall correlated with Filthy 50 (s) and 1 other fieldsHigh correlation
Run 5k (s) is highly overall correlated with Fight Gone Bad and 2 other fieldsHigh correlation
Snatch (lbs) is highly overall correlated with Fight Gone BadHigh correlation
Sprint 400m (s) is highly overall correlated with Clean and Jerk (lbs) and 3 other fieldsHigh correlation
Affiliate has 4 (22.2%) missing valuesMissing
Country has 18 (100.0%) missing valuesMissing
Back Squat (lbs) has 1 (5.6%) missing valuesMissing
Clean and Jerk (lbs) has 1 (5.6%) missing valuesMissing
Deadlift (lbs) has 1 (5.6%) missing valuesMissing
Fight Gone Bad has 12 (66.7%) missing valuesMissing
Max Pull-ups has 7 (38.9%) missing valuesMissing
Chad1000x (s) has 18 (100.0%) missing valuesMissing
L1 Benchmark (s) has 18 (100.0%) missing valuesMissing
Filthy 50 (s) has 12 (66.7%) missing valuesMissing
Fran (s) has 5 (27.8%) missing valuesMissing
Grace (s) has 6 (33.3%) missing valuesMissing
Helen (s) has 11 (61.1%) missing valuesMissing
Run 5k (s) has 7 (38.9%) missing valuesMissing
Sprint 400m (s) has 12 (66.7%) missing valuesMissing
Athlete has unique valuesUnique
Rank has unique valuesUnique
Country is an unsupported type, check if it needs cleaning or further analysisUnsupported
Chad1000x (s) is an unsupported type, check if it needs cleaning or further analysisUnsupported
L1 Benchmark (s) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-02-17 20:22:43.486622
Analysis finished2024-02-17 20:22:58.434966
Duration14.95 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Athlete
Text

UNIQUE 

Distinct18
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size288.0 B
2024-02-17T15:22:58.546717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length25
Median length16
Mean length14.055556
Min length10

Characters and Unicode

Total characters253
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)100.0%

Sample

1st rowJeffrey Adler
2nd rowPatrick Vellner
3rd rowRoman Khrennikov
4th rowBrent Fikowski
5th rowJonne Koski
ValueCountFrequency (%)
samuel 2
 
5.4%
jeffrey 1
 
2.7%
ohlsen 1
 
2.7%
will 1
 
2.7%
moorad 1
 
2.7%
björgvin 1
 
2.7%
karl 1
 
2.7%
guðmundsson 1
 
2.7%
chandler 1
 
2.7%
smith 1
 
2.7%
Other values (26) 26
70.3%
2024-02-17T15:22:58.841294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 24
 
9.5%
19
 
7.5%
n 18
 
7.1%
i 17
 
6.7%
l 16
 
6.3%
r 16
 
6.3%
a 15
 
5.9%
o 15
 
5.9%
s 9
 
3.6%
k 8
 
3.2%
Other values (34) 96
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197
77.9%
Uppercase Letter 37
 
14.6%
Space Separator 19
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 24
12.2%
n 18
 
9.1%
i 17
 
8.6%
l 16
 
8.1%
r 16
 
8.1%
a 15
 
7.6%
o 15
 
7.6%
s 9
 
4.6%
k 8
 
4.1%
u 7
 
3.6%
Other values (17) 52
26.4%
Uppercase Letter
ValueCountFrequency (%)
S 5
13.5%
K 4
10.8%
B 3
 
8.1%
M 3
 
8.1%
J 3
 
8.1%
C 3
 
8.1%
F 2
 
5.4%
V 2
 
5.4%
U 2
 
5.4%
N 2
 
5.4%
Other values (6) 8
21.6%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 234
92.5%
Common 19
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 24
 
10.3%
n 18
 
7.7%
i 17
 
7.3%
l 16
 
6.8%
r 16
 
6.8%
a 15
 
6.4%
o 15
 
6.4%
s 9
 
3.8%
k 8
 
3.4%
u 7
 
3.0%
Other values (33) 89
38.0%
Common
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 251
99.2%
None 2
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 24
 
9.6%
19
 
7.6%
n 18
 
7.2%
i 17
 
6.8%
l 16
 
6.4%
r 16
 
6.4%
a 15
 
6.0%
o 15
 
6.0%
s 9
 
3.6%
k 8
 
3.2%
Other values (32) 94
37.5%
None
ValueCountFrequency (%)
ö 1
50.0%
ð 1
50.0%

Affiliate
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing4
Missing (%)22.2%
Memory size288.0 B
2024-02-17T15:22:58.971003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length18.071429
Min length12

Characters and Unicode

Total characters253
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)100.0%

Sample

1st rowCrossFit Wonderland
2nd rowCrossFit Nanaimo
3rd rowCrossFit Mayhem
4th rowCrossFit 10K
5th rowCrossFit Invictus
ValueCountFrequency (%)
crossfit 14
41.2%
minnetonka 1
 
2.9%
spokane 1
 
2.9%
house 1
 
2.9%
strong 1
 
2.9%
naples 1
 
2.9%
chambly 1
 
2.9%
adm 1
 
2.9%
pauli 1
 
2.9%
sankt 1
 
2.9%
Other values (11) 11
32.4%
2024-02-17T15:22:59.204715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 33
13.0%
o 20
 
7.9%
i 20
 
7.9%
20
 
7.9%
t 19
 
7.5%
r 16
 
6.3%
C 15
 
5.9%
F 14
 
5.5%
a 14
 
5.5%
n 11
 
4.3%
Other values (29) 71
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 179
70.8%
Uppercase Letter 49
 
19.4%
Space Separator 20
 
7.9%
Decimal Number 5
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 33
18.4%
o 20
11.2%
i 20
11.2%
t 19
10.6%
r 16
8.9%
a 14
7.8%
n 11
 
6.1%
l 10
 
5.6%
e 10
 
5.6%
k 4
 
2.2%
Other values (10) 22
12.3%
Uppercase Letter
ValueCountFrequency (%)
C 15
30.6%
F 14
28.6%
M 3
 
6.1%
S 3
 
6.1%
N 3
 
6.1%
P 2
 
4.1%
H 2
 
4.1%
A 1
 
2.0%
D 1
 
2.0%
E 1
 
2.0%
Other values (4) 4
 
8.2%
Decimal Number
ValueCountFrequency (%)
0 2
40.0%
6 1
20.0%
1 1
20.0%
3 1
20.0%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 228
90.1%
Common 25
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 33
14.5%
o 20
 
8.8%
i 20
 
8.8%
t 19
 
8.3%
r 16
 
7.0%
C 15
 
6.6%
F 14
 
6.1%
a 14
 
6.1%
n 11
 
4.8%
l 10
 
4.4%
Other values (24) 56
24.6%
Common
ValueCountFrequency (%)
20
80.0%
0 2
 
8.0%
6 1
 
4.0%
1 1
 
4.0%
3 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 33
13.0%
o 20
 
7.9%
i 20
 
7.9%
20
 
7.9%
t 19
 
7.5%
r 16
 
6.3%
C 15
 
5.9%
F 14
 
5.5%
a 14
 
5.5%
n 11
 
4.3%
Other values (29) 71
28.1%

Country
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18
Missing (%)100.0%
Memory size288.0 B

Region
Categorical

CONSTANT 

Distinct1
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size288.0 B
worldwide
18 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters162
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowworldwide
2nd rowworldwide
3rd rowworldwide
4th rowworldwide
5th rowworldwide

Common Values

ValueCountFrequency (%)
worldwide 18
100.0%

Length

2024-02-17T15:22:59.433074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:22:59.574272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
worldwide 18
100.0%

Most occurring characters

ValueCountFrequency (%)
w 36
22.2%
d 36
22.2%
o 18
11.1%
r 18
11.1%
l 18
11.1%
i 18
11.1%
e 18
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 162
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 36
22.2%
d 36
22.2%
o 18
11.1%
r 18
11.1%
l 18
11.1%
i 18
11.1%
e 18
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 36
22.2%
d 36
22.2%
o 18
11.1%
r 18
11.1%
l 18
11.1%
i 18
11.1%
e 18
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 36
22.2%
d 36
22.2%
o 18
11.1%
r 18
11.1%
l 18
11.1%
i 18
11.1%
e 18
11.1%

Division
Categorical

CONSTANT 

Distinct1
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size288.0 B
Men
18 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMen
2nd rowMen
3rd rowMen
4th rowMen
5th rowMen

Common Values

ValueCountFrequency (%)
Men 18
100.0%

Length

2024-02-17T15:22:59.686745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:22:59.818661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
men 18
100.0%

Most occurring characters

ValueCountFrequency (%)
M 18
33.3%
e 18
33.3%
n 18
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36
66.7%
Uppercase Letter 18
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 18
50.0%
n 18
50.0%
Uppercase Letter
ValueCountFrequency (%)
M 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 18
33.3%
e 18
33.3%
n 18
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 18
33.3%
e 18
33.3%
n 18
33.3%

Rank
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct18
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.611111
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:22:59.969736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.85
Q16.25
median15.5
Q321.75
95-th percentile34.15
Maximum35
Range34
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation10.555951
Coefficient of variation (CV)0.67618192
Kurtosis-0.80431202
Mean15.611111
Median Absolute Deviation (MAD)9
Skewness0.29008719
Sum281
Variance111.4281
MonotonicityStrictly increasing
2024-02-17T15:23:00.115538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 1
 
5.6%
2 1
 
5.6%
34 1
 
5.6%
26 1
 
5.6%
25 1
 
5.6%
22 1
 
5.6%
21 1
 
5.6%
20 1
 
5.6%
19 1
 
5.6%
16 1
 
5.6%
Other values (8) 8
44.4%
ValueCountFrequency (%)
1 1
5.6%
2 1
5.6%
3 1
5.6%
4 1
5.6%
6 1
5.6%
7 1
5.6%
11 1
5.6%
14 1
5.6%
15 1
5.6%
16 1
5.6%
ValueCountFrequency (%)
35 1
5.6%
34 1
5.6%
26 1
5.6%
25 1
5.6%
22 1
5.6%
21 1
5.6%
20 1
5.6%
19 1
5.6%
16 1
5.6%
15 1
5.6%

Games_Level
Categorical

CONSTANT 

Distinct1
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size288.0 B
worldwide
18 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters162
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowworldwide
2nd rowworldwide
3rd rowworldwide
4th rowworldwide
5th rowworldwide

Common Values

ValueCountFrequency (%)
worldwide 18
100.0%

Length

2024-02-17T15:23:00.249910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:23:00.358714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
worldwide 18
100.0%

Most occurring characters

ValueCountFrequency (%)
w 36
22.2%
d 36
22.2%
o 18
11.1%
r 18
11.1%
l 18
11.1%
i 18
11.1%
e 18
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 162
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 36
22.2%
d 36
22.2%
o 18
11.1%
r 18
11.1%
l 18
11.1%
i 18
11.1%
e 18
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 36
22.2%
d 36
22.2%
o 18
11.1%
r 18
11.1%
l 18
11.1%
i 18
11.1%
e 18
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 36
22.2%
d 36
22.2%
o 18
11.1%
r 18
11.1%
l 18
11.1%
i 18
11.1%
e 18
11.1%

Qualifier
Categorical

CONSTANT 

Distinct1
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size288.0 B
games
18 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters90
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgames
2nd rowgames
3rd rowgames
4th rowgames
5th rowgames

Common Values

ValueCountFrequency (%)
games 18
100.0%

Length

2024-02-17T15:23:00.463448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:23:00.631735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
games 18
100.0%

Most occurring characters

ValueCountFrequency (%)
g 18
20.0%
a 18
20.0%
m 18
20.0%
e 18
20.0%
s 18
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 18
20.0%
a 18
20.0%
m 18
20.0%
e 18
20.0%
s 18
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 18
20.0%
a 18
20.0%
m 18
20.0%
e 18
20.0%
s 18
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 18
20.0%
a 18
20.0%
m 18
20.0%
e 18
20.0%
s 18
20.0%

Back Squat (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)76.5%
Missing1
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean466.84059
Minimum435
Maximum545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:23:00.753080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum435
5-th percentile439.7392
Q1455
median460
Q3475
95-th percentile505
Maximum545
Range110
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.437804
Coefficient of variation (CV)0.054489273
Kurtosis5.0235259
Mean466.84059
Median Absolute Deviation (MAD)13.9933
Skewness1.8815882
Sum7936.29
Variance647.08189
MonotonicityNot monotonic
2024-02-17T15:23:00.881418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
475 2
11.1%
455 2
11.1%
460 2
11.1%
465 2
11.1%
473.9933 1
 
5.6%
495 1
 
5.6%
445 1
 
5.6%
545 1
 
5.6%
456.35634 1
 
5.6%
485.0164 1
 
5.6%
Other values (3) 3
16.7%
ValueCountFrequency (%)
435 1
5.6%
440.924 1
5.6%
445 1
5.6%
450 1
5.6%
455 2
11.1%
456.35634 1
5.6%
460 2
11.1%
465 2
11.1%
473.9933 1
5.6%
475 2
11.1%
ValueCountFrequency (%)
545 1
5.6%
495 1
5.6%
485.0164 1
5.6%
475 2
11.1%
473.9933 1
5.6%
465 2
11.1%
460 2
11.1%
456.35634 1
5.6%
455 2
11.1%
450 1
5.6%

Clean and Jerk (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)70.6%
Missing1
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean359.50086
Minimum335
Maximum380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:23:00.997585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum335
5-th percentile339
Q1345
median360
Q3374.7854
95-th percentile377.6
Maximum380
Range45
Interquartile range (IQR)29.7854

Descriptive statistics

Standard deviation14.989064
Coefficient of variation (CV)0.041694098
Kurtosis-1.4173787
Mean359.50086
Median Absolute Deviation (MAD)15
Skewness-0.1514054
Sum6111.5146
Variance224.67204
MonotonicityNot monotonic
2024-02-17T15:23:01.112263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
355 2
11.1%
370 2
11.1%
340 2
11.1%
360 2
11.1%
345 2
11.1%
377 1
5.6%
335 1
5.6%
380 1
5.6%
375 1
5.6%
352.7392 1
5.6%
Other values (2) 2
11.1%
ValueCountFrequency (%)
335 1
5.6%
340 2
11.1%
345 2
11.1%
352.7392 1
5.6%
355 2
11.1%
360 2
11.1%
370 2
11.1%
374.7854 1
5.6%
375 1
5.6%
376.99002 1
5.6%
ValueCountFrequency (%)
380 1
5.6%
377 1
5.6%
376.99002 1
5.6%
375 1
5.6%
374.7854 1
5.6%
370 2
11.1%
360 2
11.1%
355 2
11.1%
352.7392 1
5.6%
345 2
11.1%

Deadlift (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)82.4%
Missing1
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean554.05783
Minimum489.42564
Maximum635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:23:01.219405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum489.42564
5-th percentile501.88513
Q1530
median540
Q3573.2012
95-th percentile619
Maximum635
Range145.57436
Interquartile range (IQR)43.2012

Descriptive statistics

Standard deviation40.567272
Coefficient of variation (CV)0.073218481
Kurtosis-0.52872572
Mean554.05783
Median Absolute Deviation (MAD)27
Skewness0.49874502
Sum9418.983
Variance1645.7036
MonotonicityNot monotonic
2024-02-17T15:23:01.320227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
530 3
16.7%
573.2012 2
11.1%
567 1
 
5.6%
595 1
 
5.6%
635 1
 
5.6%
520 1
 
5.6%
525 1
 
5.6%
540 1
 
5.6%
605 1
 
5.6%
551.155 1
 
5.6%
Other values (4) 4
22.2%
ValueCountFrequency (%)
489.42564 1
 
5.6%
505 1
 
5.6%
520 1
 
5.6%
525 1
 
5.6%
530 3
16.7%
535 1
 
5.6%
540 1
 
5.6%
551.155 1
 
5.6%
567 1
 
5.6%
573.2012 2
11.1%
ValueCountFrequency (%)
635 1
 
5.6%
615 1
 
5.6%
605 1
 
5.6%
595 1
 
5.6%
573.2012 2
11.1%
567 1
 
5.6%
551.155 1
 
5.6%
540 1
 
5.6%
535 1
 
5.6%
530 3
16.7%

Snatch (lbs)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.79207
Minimum275
Maximum313.05604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:23:01.419823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum275
5-th percentile275
Q1281.25
median290
Q3300
95-th percentile310.45841
Maximum313.05604
Range38.05604
Interquartile range (IQR)18.75

Descriptive statistics

Standard deviation12.38097
Coefficient of variation (CV)0.042430797
Kurtosis-1.250158
Mean291.79207
Median Absolute Deviation (MAD)10
Skewness0.14895263
Sum5252.2572
Variance153.28842
MonotonicityNot monotonic
2024-02-17T15:23:01.523407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
300 3
16.7%
290 2
11.1%
280 2
11.1%
275 2
11.1%
305 2
11.1%
285 2
11.1%
286 1
 
5.6%
310 1
 
5.6%
275.5775 1
 
5.6%
297.6237 1
 
5.6%
ValueCountFrequency (%)
275 2
11.1%
275.5775 1
 
5.6%
280 2
11.1%
285 2
11.1%
286 1
 
5.6%
290 2
11.1%
297.6237 1
 
5.6%
300 3
16.7%
305 2
11.1%
310 1
 
5.6%
ValueCountFrequency (%)
313.05604 1
 
5.6%
310 1
 
5.6%
305 2
11.1%
300 3
16.7%
297.6237 1
 
5.6%
290 2
11.1%
286 1
 
5.6%
285 2
11.1%
280 2
11.1%
275.5775 1
 
5.6%

Fight Gone Bad
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing12
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean470
Minimum389
Maximum522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:23:01.623992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum389
5-th percentile397
Q1430
median484
Q3517.75
95-th percentile521.5
Maximum522
Range133
Interquartile range (IQR)87.75

Descriptive statistics

Standard deviation56.59682
Coefficient of variation (CV)0.12041877
Kurtosis-1.7999961
Mean470
Median Absolute Deviation (MAD)37
Skewness-0.52417073
Sum2820
Variance3203.2
MonotonicityNot monotonic
2024-02-17T15:23:01.724740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
511 1
 
5.6%
389 1
 
5.6%
421 1
 
5.6%
520 1
 
5.6%
522 1
 
5.6%
457 1
 
5.6%
(Missing) 12
66.7%
ValueCountFrequency (%)
389 1
5.6%
421 1
5.6%
457 1
5.6%
511 1
5.6%
520 1
5.6%
522 1
5.6%
ValueCountFrequency (%)
522 1
5.6%
520 1
5.6%
511 1
5.6%
457 1
5.6%
421 1
5.6%
389 1
5.6%

Max Pull-ups
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)81.8%
Missing7
Missing (%)38.9%
Infinite0
Infinite (%)0.0%
Mean69.181818
Minimum54
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:23:01.816644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile57
Q164.5
median72
Q373
95-th percentile80
Maximum85
Range31
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation8.3164678
Coefficient of variation (CV)0.12021176
Kurtosis0.65244773
Mean69.181818
Median Absolute Deviation (MAD)4
Skewness-0.03257908
Sum761
Variance69.163636
MonotonicityNot monotonic
2024-02-17T15:23:01.908743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
72 3
16.7%
54 1
 
5.6%
64 1
 
5.6%
75 1
 
5.6%
85 1
 
5.6%
65 1
 
5.6%
74 1
 
5.6%
60 1
 
5.6%
68 1
 
5.6%
(Missing) 7
38.9%
ValueCountFrequency (%)
54 1
 
5.6%
60 1
 
5.6%
64 1
 
5.6%
65 1
 
5.6%
68 1
 
5.6%
72 3
16.7%
74 1
 
5.6%
75 1
 
5.6%
85 1
 
5.6%
ValueCountFrequency (%)
85 1
 
5.6%
75 1
 
5.6%
74 1
 
5.6%
72 3
16.7%
68 1
 
5.6%
65 1
 
5.6%
64 1
 
5.6%
60 1
 
5.6%
54 1
 
5.6%

Chad1000x (s)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18
Missing (%)100.0%
Memory size288.0 B

L1 Benchmark (s)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18
Missing (%)100.0%
Memory size288.0 B

Filthy 50 (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing12
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean1002.8333
Minimum795
Maximum1216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:23:02.004108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum795
5-th percentile819.75
Q1899.75
median983.5
Q31121.25
95-th percentile1198.25
Maximum1216
Range421
Interquartile range (IQR)221.5

Descriptive statistics

Standard deviation161.42047
Coefficient of variation (CV)0.1609644
Kurtosis-1.5706486
Mean1002.8333
Median Absolute Deviation (MAD)125.5
Skewness0.1259505
Sum6017
Variance26056.567
MonotonicityNot monotonic
2024-02-17T15:23:02.094818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
917 1
 
5.6%
1216 1
 
5.6%
1145 1
 
5.6%
1050 1
 
5.6%
795 1
 
5.6%
894 1
 
5.6%
(Missing) 12
66.7%
ValueCountFrequency (%)
795 1
5.6%
894 1
5.6%
917 1
5.6%
1050 1
5.6%
1145 1
5.6%
1216 1
5.6%
ValueCountFrequency (%)
1216 1
5.6%
1145 1
5.6%
1050 1
5.6%
917 1
5.6%
894 1
5.6%
795 1
5.6%

Fran (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)84.6%
Missing5
Missing (%)27.8%
Infinite0
Infinite (%)0.0%
Mean130.07692
Minimum109
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:23:02.180742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile115.6
Q1123
median125
Q3134
95-th percentile154.2
Maximum171
Range62
Interquartile range (IQR)11

Descriptive statistics

Standard deviation14.86305
Coefficient of variation (CV)0.11426354
Kurtosis4.6954276
Mean130.07692
Median Absolute Deviation (MAD)6
Skewness1.7649898
Sum1691
Variance220.91026
MonotonicityNot monotonic
2024-02-17T15:23:02.279696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
134 2
 
11.1%
123 2
 
11.1%
122 1
 
5.6%
120 1
 
5.6%
143 1
 
5.6%
132 1
 
5.6%
124 1
 
5.6%
109 1
 
5.6%
125 1
 
5.6%
171 1
 
5.6%
(Missing) 5
27.8%
ValueCountFrequency (%)
109 1
5.6%
120 1
5.6%
122 1
5.6%
123 2
11.1%
124 1
5.6%
125 1
5.6%
131 1
5.6%
132 1
5.6%
134 2
11.1%
143 1
5.6%
ValueCountFrequency (%)
171 1
5.6%
143 1
5.6%
134 2
11.1%
132 1
5.6%
131 1
5.6%
125 1
5.6%
124 1
5.6%
123 2
11.1%
122 1
5.6%
120 1
5.6%

Grace (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)75.0%
Missing6
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean81.666667
Minimum64
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:23:02.371546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile66.75
Q170
median76
Q390
95-th percentile112.85
Maximum120
Range56
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.041838
Coefficient of variation (CV)0.20867557
Kurtosis1.1163279
Mean81.666667
Median Absolute Deviation (MAD)6.5
Skewness1.3520883
Sum980
Variance290.42424
MonotonicityNot monotonic
2024-02-17T15:23:02.477964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
76 2
 
11.1%
70 2
 
11.1%
90 2
 
11.1%
77 1
 
5.6%
64 1
 
5.6%
120 1
 
5.6%
107 1
 
5.6%
69 1
 
5.6%
71 1
 
5.6%
(Missing) 6
33.3%
ValueCountFrequency (%)
64 1
5.6%
69 1
5.6%
70 2
11.1%
71 1
5.6%
76 2
11.1%
77 1
5.6%
90 2
11.1%
107 1
5.6%
120 1
5.6%
ValueCountFrequency (%)
120 1
5.6%
107 1
5.6%
90 2
11.1%
77 1
5.6%
76 2
11.1%
71 1
5.6%
70 2
11.1%
69 1
5.6%
64 1
5.6%

Helen (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing11
Missing (%)61.1%
Infinite0
Infinite (%)0.0%
Mean453.57143
Minimum422
Maximum586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:23:02.628458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum422
5-th percentile423.8
Q1430
median434
Q3436.5
95-th percentile541.6
Maximum586
Range164
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation58.62837
Coefficient of variation (CV)0.1292594
Kurtosis6.8254293
Mean453.57143
Median Absolute Deviation (MAD)4
Skewness2.6011599
Sum3175
Variance3437.2857
MonotonicityNot monotonic
2024-02-17T15:23:02.795764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
438 1
 
5.6%
586 1
 
5.6%
428 1
 
5.6%
435 1
 
5.6%
422 1
 
5.6%
434 1
 
5.6%
432 1
 
5.6%
(Missing) 11
61.1%
ValueCountFrequency (%)
422 1
5.6%
428 1
5.6%
432 1
5.6%
434 1
5.6%
435 1
5.6%
438 1
5.6%
586 1
5.6%
ValueCountFrequency (%)
586 1
5.6%
438 1
5.6%
435 1
5.6%
434 1
5.6%
432 1
5.6%
428 1
5.6%
422 1
5.6%

Run 5k (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)81.8%
Missing7
Missing (%)38.9%
Infinite0
Infinite (%)0.0%
Mean1161.8182
Minimum1085
Maximum1380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-02-17T15:23:02.986362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1085
5-th percentile1107.5
Q11132.5
median1139
Q31160
95-th percentile1274
Maximum1380
Range295
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation75.781024
Coefficient of variation (CV)0.065226234
Kurtosis8.6416914
Mean1161.8182
Median Absolute Deviation (MAD)16
Skewness2.7589418
Sum12780
Variance5742.7636
MonotonicityNot monotonic
2024-02-17T15:23:03.151026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1130 2
 
11.1%
1160 2
 
11.1%
1155 1
 
5.6%
1138 1
 
5.6%
1085 1
 
5.6%
1139 1
 
5.6%
1380 1
 
5.6%
1168 1
 
5.6%
1135 1
 
5.6%
(Missing) 7
38.9%
ValueCountFrequency (%)
1085 1
5.6%
1130 2
11.1%
1135 1
5.6%
1138 1
5.6%
1139 1
5.6%
1155 1
5.6%
1160 2
11.1%
1168 1
5.6%
1380 1
5.6%
ValueCountFrequency (%)
1380 1
5.6%
1168 1
5.6%
1160 2
11.1%
1155 1
5.6%
1139 1
5.6%
1138 1
5.6%
1135 1
5.6%
1130 2
11.1%
1085 1
5.6%

Sprint 400m (s)
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)50.0%
Missing12
Missing (%)66.7%
Memory size288.0 B
59.0
54.0
66.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters24
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)16.7%

Sample

1st row59.0
2nd row66.0
3rd row59.0
4th row59.0
5th row54.0

Common Values

ValueCountFrequency (%)
59.0 3
 
16.7%
54.0 2
 
11.1%
66.0 1
 
5.6%
(Missing) 12
66.7%

Length

2024-02-17T15:23:03.304583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:23:03.435816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
59.0 3
50.0%
54.0 2
33.3%
66.0 1
 
16.7%

Most occurring characters

ValueCountFrequency (%)
. 6
25.0%
0 6
25.0%
5 5
20.8%
9 3
12.5%
4 2
 
8.3%
6 2
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18
75.0%
Other Punctuation 6
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6
33.3%
5 5
27.8%
9 3
16.7%
4 2
 
11.1%
6 2
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 6
25.0%
0 6
25.0%
5 5
20.8%
9 3
12.5%
4 2
 
8.3%
6 2
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 6
25.0%
0 6
25.0%
5 5
20.8%
9 3
12.5%
4 2
 
8.3%
6 2
 
8.3%

Interactions

2024-02-17T15:22:56.462932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:43.937208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.959477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.928359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.991574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.979573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:50.583003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.655881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.636665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.606895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.562910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.507857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:56.554009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.040085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.041646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.010392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.083221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:48.065004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:50.667016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.738538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.716357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.688881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.644235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.589416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:56.634634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.120385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.116622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.086157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.162425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:48.145063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:50.748002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.816253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.800184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.766939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.719589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.663478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:56.715084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.197253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.189911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.176453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.240114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:48.221714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:50.839946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.893232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.889549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.841096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.791370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.747056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:56.809577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.283556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.271968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.278646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.324139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:48.312599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:50.922531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.977547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.970006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.926275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.876907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.830295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:56.898494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.371130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.357107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.375374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.411450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:48.401541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.044079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.063020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.045428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.012145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.959209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.910589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:56.989075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.469931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.436434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.472203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.492046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:48.486411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.125345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.147679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.123370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.095205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.043936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.989953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:57.071687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.552144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.520394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.564621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.576952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:48.569848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.217340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.246367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.207707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.175148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.121764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:56.069826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:57.160030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.627074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.593474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.661913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.651077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:48.643378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.295767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.322512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.282500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.248314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.203981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:56.148597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:57.240170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.709563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.678431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.753759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.734276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:48.726198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.380580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.404144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.361524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.327132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.281143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:56.226202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:57.316903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.794013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.770167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.832701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.813033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:50.371604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.469220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.479887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.447048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.404004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.354384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:56.302033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:57.404704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:44.878127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:45.850816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:46.916418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:47.895764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:50.467918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:51.568304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:52.560451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:53.525716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:54.481987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:55.431697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:22:56.380054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-17T15:23:03.571583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Back Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Fight Gone BadFilthy 50 (s)Fran (s)Grace (s)Helen (s)Max Pull-upsRankRun 5k (s)Snatch (lbs)Sprint 400m (s)
Back Squat (lbs)1.0000.4450.5900.353-0.647-0.628-0.5560.3190.378-0.133-0.044-0.0060.000
Clean and Jerk (lbs)0.4451.0000.389-0.5430.200-0.665-0.4680.493-0.111-0.018-0.0370.4680.707
Deadlift (lbs)0.5900.3891.000-0.7140.257-0.168-0.2590.2570.422-0.240-0.4570.1380.707
Fight Gone Bad0.353-0.543-0.7141.000-0.7710.145-0.319-0.5000.0300.3710.928-0.6380.451
Filthy 50 (s)-0.6470.2000.257-0.7711.0000.0290.7830.300-0.030-0.600-0.8120.1740.000
Fran (s)-0.628-0.665-0.1680.1450.0291.0000.453-0.530-0.2210.2560.1070.2050.000
Grace (s)-0.556-0.468-0.259-0.3190.7830.4531.000-0.086-0.189-0.130-0.338-0.2280.509
Helen (s)0.3190.4930.257-0.5000.300-0.530-0.0861.000-0.580-0.571-0.2570.0730.816
Max Pull-ups0.378-0.1110.4220.030-0.030-0.221-0.189-0.5801.0000.083-0.548-0.2540.451
Rank-0.133-0.018-0.2400.371-0.6000.256-0.130-0.5710.0831.0000.3240.0820.333
Run 5k (s)-0.044-0.037-0.4570.928-0.8120.107-0.338-0.257-0.5480.3241.0000.1820.000
Snatch (lbs)-0.0060.4680.138-0.6380.1740.205-0.2280.073-0.2540.0820.1821.0000.000
Sprint 400m (s)0.0000.7070.7070.4510.0000.0000.5090.8160.4510.3330.0000.0001.000

Missing values

2024-02-17T15:22:57.558594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-17T15:22:57.941739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-17T15:22:58.258274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
57069Jeffrey AdlerCrossFit WonderlandNaNworldwideMen1.0worldwidegames475.0000377.0567.0000290.0511.054.0NaNNaN917.0122.076.0438.01155.059.0
57070Patrick VellnerCrossFit NanaimoNaNworldwideMen2.0worldwidegames455.0000355.0595.0000290.0389.064.0NaNNaN1216.0134.077.0586.01130.066.0
57073Roman KhrennikovCrossFit MayhemNaNworldwideMen3.0worldwidegames473.9933370.0573.2012280.0NaN75.0NaNNaNNaN120.070.0NaNNaNNaN
57076Brent FikowskiNaNNaNworldwideMen4.0worldwidegamesNaNNaNNaN300.0NaNNaNNaNNaNNaNNaNNaN428.0NaNNaN
57080Jonne KoskiCrossFit 10KNaNworldwideMen6.0worldwidegames455.0000340.0530.0000275.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
57082Chandler SmithCrossFit InvictusNaNworldwideMen7.0worldwidegames495.0000360.0635.0000305.0NaN85.0NaNNaNNaN123.064.0435.01138.0NaN
57091Björgvin Karl GuðmundssonCrossFit HengillNaNworldwideMen11.0worldwidegames445.0000335.0520.0000286.0NaN65.0NaNNaNNaN143.0120.0NaN1160.0NaN
57097Will MooradCrossFit East NashvilleNaNworldwideMen14.0worldwidegames460.0000355.0525.0000310.0NaNNaNNaNNaNNaN132.0NaNNaNNaNNaN
57098Samuel KwantNaNNaNworldwideMen15.0worldwidegames460.0000345.0530.0000285.0NaN74.0NaNNaNNaN124.0107.0NaN1085.0NaN
57102Noah OhlsenPeak 360 CrossFitNaNworldwideMen16.0worldwidegames465.0000380.0540.0000285.0421.072.0NaNNaN1145.0109.076.0NaN1130.059.0
AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
57098Samuel KwantNaNNaNworldwideMen15.0worldwidegames460.00000345.00000530.00000285.00000NaN74.0NaNNaNNaN124.0107.0NaN1085.0NaN
57102Noah OhlsenPeak 360 CrossFitNaNworldwideMen16.0worldwidegames465.00000380.00000540.00000285.00000421.072.0NaNNaN1145.0109.076.0NaN1130.059.0
57111Nick MathewCrossFit MinnetonkaNaNworldwideMen19.0worldwidegames545.00000375.00000605.00000300.00000NaNNaNNaNNaNNaNNaNNaNNaN1160.0NaN
57115Uldis UpenieksNaNNaNworldwideMen20.0worldwidegames456.35634352.73920551.15500275.57750NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
57117Moritz FiebigCrossFit Sankt PauliNaNworldwideMen21.0worldwidegames485.01640376.99002573.20120297.62370NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
57119Samuel CournoyerCrossFit ADM ChamblyNaNworldwideMen22.0worldwidegames435.00000370.00000530.00000305.00000NaNNaNNaNNaNNaN125.090.0NaNNaNNaN
57126James SpragueCrossFit NaplesNaNworldwideMen25.0worldwidegames450.00000340.00000535.00000280.00000520.072.0NaNNaN1050.0171.090.0422.01139.059.0
57130Bronislaw OlenkowiczCrossFit Strong HouseNaNworldwideMen26.0worldwidegames440.92400374.78540489.42564313.05604NaN60.0NaNNaNNaN131.069.0NaN1380.0NaN
57149Cole SagerCrossFit Spokane ValleyNaNworldwideMen34.0worldwidegames475.00000345.00000505.00000275.00000522.068.0NaNNaN795.0123.070.0434.01168.054.0
57151Alex VigneaultNaNNaNworldwideMen35.0worldwidegames465.00000360.00000615.00000300.00000457.072.0NaNNaN894.0134.071.0432.01135.054.0